Browsing by Subject "Hazelnuts"

Now showing 1 - 3 of 3

Open Access
Classification of agricultural kernels using impact acoustic signal processing
(2006) Onaran, İbrahim
The quality is the main factor that directly affects the price for many agricultural produces. The quality depends on different properties of the produce. Most important property is associated with health of consumers. Other properties mostly depend on the type of concerned vegetable. For instance, emptiness is important for hazelnuts while openness is crucial for the pistachio nuts. Therefore, the agricultural produces should be separated according to their quality to maintain the consumers health and increase the price of the produce in international trades. Current approaches are mostly based on invasive chemical analysis of some selected food items or sorting food items according to their color. Although chemical analysis gives the most accurate results, it is impossible to analyze large quantities of food items. The impact sound signal processing can be used to classify these produces according to their quality. These methods are inexpensive, noninvasive and most of all they can be applied in real-time to process large amount of food. Several signal processing methods for extracting impact sound features are proposed to classify the produces according to their quality. These methods are including time and frequency domain methods. Several time and frequency domain methods including Weibull parameters, maximum points and variances in time windows, DFT (Discrete Fourier Transform) coefficients around the maximum spectral points etc. are used to extract the features from the impact sound. In this study, we used hazelnut and wheat kernel impact sounds. The success rate over 90% is achieved for all types produces.
Open Access
Signal and image processing algorithms for agricultural applications
(2006) Dülek, Berkan
Medical studies indicate that acrylamide causes cancer in animals and certain doses of acrylamide are toxic to the nervous system of both animals and humans. Acrylamide is produced in carbohydrate foods prepared at high temperatures such as fried potatoes. For this reason, it is crucial for human health to quantitatively measure the amount of acrylamide formed as a result of prolonged cooking at high temperatures. In this thesis, a correlation is demonstrated between measured acrylamide concentrations and NABY (Normalized Area of Brownish Yellow regions) values estimated from surface color properties of fried potato images using a modified form of the k-means algorithm. Same method is used to estimate acrylamide levels of roasted coffee beans. The proposed method seems to be a promising approach for the estimation of acrylamide levels and can find applications in industrial systems. The quality and price of hazelnuts are mainly determined by the ratio of shell weight to kernel weight. Due to a number of physiological and physical disorders, hazelnuts may grow without fully developed kernels. We previously proposed a prototype system which detects empty hazelnuts by dropping them onto a steel plate and processing the acoustic signal generated when kernels hit the plate. In that study, feature vectors describing time and frequency nature of the impact sound were extracted from the acoustic signal and classified using Support Vector Machines. In the second part of this thesis, a feature domain post-processing method based on vector median/mean filtering is shown to further increase these classification results.
Open Access
System for removing shell pieces hazelnut kernels using impact vibration analysis
(Elsevier BV, 2014-02) Çetin, A. Enis; Pearson, T. C.; Sevimli, R. A.
A system for removing shell pieces from hazelnut kernels using impact vibration analysis was developed in which nuts are dropped onto a steel plate and the vibration signals are captured and analyzed. The mel-cepstral feature parameters, line spectral frequency values, and Fourier-domain Lebesgue features were extracted from the vibration signals. The best experimental results were obtained using the melcepstral feature parameters. The feature parameters were classified using a support vector machine (SVM), which was trained a priori using a manually classified dataset. An average recognition rate of 98.2% was achieved. An important feature of the method is that it is easily trainable, enabling it to be applicable to other nuts, including walnuts and pistachio nuts. In addition, the system can be implemented in real time. 2013 Elsevier B.V. All rights reserved